Myoelectric control systems - A survey

By Mohammadreza Asghari Oskoei, Huosheng Hu. Biomedical Signal Processing and Control, 2007

1,251 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience

View Paper: https://www.semanticscholar.org/paper/411f872a41489170e8ddcbc453a2a150b28609ce

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

Citations in the corpus

Gesture recognition by instantaneous surface EMG images (2016, neutral): “It has generally been accepted that feeding the instantaneous value of myoelectric signals directly to a classifier is impractical and useless for pattern recognition techniques 5,6 .”
Surface Electromyography Signal Processing and Classification Techniques (2013, methodology): “PCA is a more well-known method than other methods of dimension reduction [90].”
A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors (2011, methodology): “Although previous studies on EMG-based HCI have attained relatively good results, it has a significant distance from commercial applications for fine control due to some problems, including the separability and reproducibility of EMG measurements [5].”
Mean and Median Frequency of EMG Signal to Determine Muscle Force based on Time- dependent Power Spectrum (2013, methodology): “Throughout this procedure, a feature extraction is one of the most important steps [2].”
Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, neutral): “[15] 2002 MLP TD set (4) Patients 240 4 2 >90”
A Novel Feature Extraction for Robust EMG Pattern Recognition (2009, methodology): “Main application of these fields is the control of the prosthesis or other assistive devices using the different patterns of sEMG signal [1-2].”
EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, methodology): “Myoelectric control systems (MCSs) have been used to control assistive and rehabilitation devices for many years by conducting the classified patterns of surface electromyography (EMG) signal (Oskoei & Hu, 2007; Peerdeman et al., 2011; Zecca, Micera, Carrozza & Dario, 2002).”
Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, background): “Pattern recognition techniques have long been proposed as a way of extracting considerable information from EMG sensors for recognition of human activity [1,2], and have the potential to restore more degrees of freedom than conventional control techniques.”
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, methodology): “As alternative to conventional control algorithms implemented in commercial devices, pattern recognition methods have been applied for several decades for classifying EMG signals [10], [11].”
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “The sEMG signals, which are non-stationary, represent the sum of subcutaneous motor action potentials generated through muscular contraction [1].”
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, methodology): “According to Englehart [63], there are two main strategies for dimensionality reduction: Feature projection methods attempt to determine the best combination of the original features to form a new feature set which is generally smaller than the original one [45].”
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition (2018, background): “Existing sEMG-based hand-crafted features can be divided into three categories [38]: time domain, frequency domain, and time-frequency domain features.”
Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb (2008, neutral): “Digital Object Identifier 10.1109/TBME.2008.919734 The application of pattern recognition to myoelectric control schemes was first introduced in the 1960s–1970s [1], [2]; however, due to limited acquisition instruments and computing capacity at that time, real-time control was not feasible.”
Smart wearable systems: Current status and future challenges (2012, background): “Rehabilitation – movement and muscle activity pattern recordings can be associatedwith a given set of functional motor tasks and muscle stimulation [66].”
Feature reduction and selection for EMG signal classification (2012, background): “Widespread potential applications for surface EMG signal classification and control have been reported in the last two decades; including multifunction prosthesis, electrical wheelchairs, virtual mouse and keyboard, and virtual worlds (Oskoei & Hu, 2007).”